Shape recognition is a key technology in intelligent video surveillance and the automatic driving system. It can be used to classify interesting objects in intelligent video surveillance and to recognize traffic signs in the automatic driving system.
Most of the shape classification methods focus on local features of shapes. These methods work well only in the case of using discriminative local features. In fact, global features play an important role in shape classification. However most of current shape classification algorithms ignore the importance of global features. Meanwhile, some kinds of global features, e.g., attention-based ones, are difficult to be incorporated into local features based shape classification. Fusion of global features and local features is a key technology in shape classification. However, most fusion strategies simply use the concatenation of local and global representation. Usually these strategies perform not well and lack of cognitive motivations.
It's worth noting that there is related research about global features and local features in the field of cognitive visual psychology. On the one hand, early feature-analysis theories hold that visual perception is a local-to-global process. Marr's computational vision model claims that the primitives of visual information are simple components of forms and their local geometric properties. Treisman's feature integration theory (A. Treisman and G. Gelade, “A feature-integration theory of attention,” Cognitive Psychology, vol. 12, no. 1, pp. 97-136, 1980.) assumes that primary visual features are firstly represented with independent “feature maps” and later recombined together to form objects. Biederman's recognition-by-components (RBC) theory (I. Biederman, “Recognition-by-components: a theory of human image understanding,” Psychological Review, vol. 94, no. 2, pp. 115-147, 1987.) considers that object recognition originates from breaking object into components of basic shapes. On the other hand, early holistic theory, e.g., Gestalt psychology of perceptual organization http://en.wikipedia.org/wiki/Gestalt psychology.) considers that cognitive process is a global-to-local process. Along this direction, Chen et al. proposed a topological perceptual organization (TPO) theory which makes a great breakthrough in cognitive visual psychology. It develops the Gestalt psychology and uses visual psychology experiments to well support that: 1) global perception is prior to local perception; 2) global properties can be described by topological invariance; and 3) topological invariance is superior to other properties, e.g. affine invariance and scale invariance. In the following, we analyze two interesting experiments.
FIG. 9 illustrates the experiment of testing bees' ability of shape classification. In this experiment (L. Chen, S. W. Zhang, and M. Srinivasan, “Global perception in small brain: Topological pattern recognition in honeybees,” Proceedings of the National Academy of Science, vol. 100, pp. 6884-6889, 2003.), Chen et al. firstly trained bees to find the shape “O” (by a reward of sugar water). Then, the sugar water was removed and bees' ability to shape recognition was tested again. Results show that bees would choose the diamond shape which is similar to shape “O”. This experiment demonstrates that, for creatures with a low level visual system, topological invariance can still play an important role in shape classification.
In another famous experiment shown in FIG. 10, Chen et al. demonstrated that topological invariance is also the most critical for human visual perception (Y. Zhuo, T. G. Zhou, H. Y. Rao, J. J. Wang, M. Meng, M. Chen, C. Zhou, and L. Chen, “Contributions of the visual ventral pathway to long-range apparent motion,” Science, vol. 299, pp. 417-420, 2003.). In this experiment, subjects were requested to judge whether two images are different or identical after very short presentation (5 ms) of a pair of images. The high correct response indicates that two shapes are easily distinguished, i.e., topologically different. The experimental results are consistent with their prediction that it is hard for subjects to differentiate topologically equivalent structures.
Although TPO theory makes great breakthrough in the field of cognitive visual psychology, it is built on experiments and lacks of mathematic descriptions and a computational model.